基于模糊C均值聚类的风电场多机等值方法

发布时间：2018-05-06 22:23

  本文选题：风电场 + 等值模型　； 参考：《现代电力》2016年06期

【摘要】：风电场等值是含风电场接入电网分析计算的重要技术手段。为降低风电场等值的难度,提高风电场分群的效率,本文基于风电机组实际运行中的监测状态量,采用模糊C均值(FCM)聚类算法,实现了风电场等值。首先选定各机组输出有功功率、无功功率、机端电压有效值及输出电流有效值为分群指标,并根据给定的等值机台数,将风电场分群问题转化为聚类问题;其次建立了风电机组类属隶属度函数和模糊C均值聚类算法的目标函数,通过迭代求解最优的聚类中心和模糊隶属度矩阵,得到风电场分群结果,算法具有计算简单、收敛性好的特点;然后,根据分群结果,对不同群的风电机组进行等值,实现风电场的多机等值;最后,通过仿真比较验证了本方法的有效性。本方法选取的分群指标具有可实操性,且在给定等值机台数条件下,计算更为简单、等值精度更高,适合用于风电场等值的实际工程计算。
[Abstract]:Wind farm equivalence is an important technical means to connect wind farm to power network analysis and calculation. In order to reduce the difficulty of wind farm equivalence and improve the efficiency of wind farm clustering, based on the monitoring state of wind turbine in actual operation, the fuzzy C-means FCM-based clustering algorithm is used to realize wind farm equivalence. First, the output active power, reactive power, terminal voltage effective value and output current effective value of each unit are selected as cluster indexes, and the cluster problem of wind farm is transformed into clustering problem according to the given number of equivalent machines. Secondly, the membership function of wind turbine and the objective function of fuzzy C-means clustering algorithm are established. The optimal clustering center and fuzzy membership matrix are solved iteratively, and the clustering results of wind farm are obtained. The convergence is good; then, according to the cluster results, the wind turbines of different groups are equated to achieve the multi-machine equivalence of wind farms. Finally, the effectiveness of the method is verified by simulation and comparison. The cluster index selected by this method is practical, and the calculation is simpler and the equivalent precision is higher under the condition of given equivalent number of machines, so it is suitable for the practical engineering calculation of wind farm equivalence.
【作者单位】： 华南理工大学电力学院;
【分类号】：TM614
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本文编号：1854174